5 papers
MobileFineTuner: A Mobile-Native Framework for On-Device LLM Fine-Tuning in Real-World Embedded AI Applications
Jiaxiang Geng, Lunyu Zhao, Yiyi Lu +1
Large language models (LLMs) are moving from cloud-centric services toward on-device embedded AI, where models interact with private, longitudinal signals sensed from users and the…
EdgeFlowerTune: Evaluating Federated LLM Fine-Tuning Under Realistic Edge System Constraints
Jiaxiang Geng, Yiyi Lu, Lunyu Zhao +3
Federated fine-tuning offers a promising paradigm for adapting large language models (LLMs) on edge devices by leveraging the rich, diverse, and continuously generated data from sm…
When Stored Evidence Stops Being Usable: Scale-Conditioned Evaluation of Agent Memory
Jiaqi Shao, Yiyi Lu, Yunzhen Zhang +1
Memory-agent evaluations report fixed-snapshot accuracy or retrieval quality, but these scores do not show whether evidence remains usable as irrelevant sessions (sessions not anno…
AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents
Yiyi Lu, Hoi Ian Au, Junyao Zhang +8
Electronic Design Automation (EDA) remains heavily reliant on tool command language (Tcl) scripting to drive complex RTL-to-GDSII flows. This scripting-based paradigm is labor-inte…
IoT-MCP: Bridging LLMs and IoT Systems Through Model Context Protocol
Ningyuan Yang, Guanliang Lyu, Mingchen Ma +7
The integration of Large Language Models (LLMs) with Internet-of-Things (IoT) systems faces significant challenges in hardware heterogeneity and control complexity. The Model Conte…